Cybersecurity alerts flow endlessly into Security Operation Centers (SOCs) at rates no human could possibly match, yet still demand immediate response. Malicious actors are operating at machine speeds, and delays can mean devastating breaches. Staff burnout and turnover in the SOC are notoriously high. So how do you go about scaling your SOC capabilities without draining your organization?
Building the Hyperscale SOC
The answer? Increase efficiency of systems and people. The best way to do that is with Artificial Intelligence (AI)-enabled detections and threat hunting. However, AI and data science alone is no panacea — machine learning (ML) must be properly implemented and maintained, not just “bolted on” after the fact, which can lead to an ML algorithm that you can’t modify, which means your protections don’t get better over time.
Enabling the hyperscale SOC requires an approach that is centered on data science and powered to put data first, empowering human threat hunters, improving the signal-to-noise ratio through high-fidelity alerts.
Cutting through the Noise
Security teams receive thousands of alerts each day — and can address only 72% of them, with most staff only being able to handle 10-12 threats per day. When you can only get to a percentage of the alerts, it’s absolutely critical that needless false alerts are eliminated before they get to your SOC staff.
The security status quo has been sending more alerts than humans can feasibly handle, which is the case with solutions like SIEM (managed or otherwise). The goal is high-fidelity detections that are truly indicative of malicious behavior, thus empowering threat hunters to find all the attacks without a lot of false positives. This improved signal-to-noise ratio helps tremendously with the issue of alert fatigue.
To learn more about enabling advances in automation at scale, read our CXO Insights piece.